Deep domain adaptation (DDA) is a learning technique that addresses the lack of labeled data by leveraging deep networks to learn transferable representations. Unlike traditional methods that use shallow features, DDA integrates domain adaptation into the deep learning pipeline. This survey provides a comprehensive overview of DDA methods in computer vision, highlighting four key contributions: (1) a taxonomy of DDA scenarios based on domain divergence properties, (2) categorization of DDA approaches by training loss, (3) overview of applications beyond image classification, and (4) identification of current method limitations and future directions. The paper discusses traditional domain adaptation (DA) methods, which rely on labeled data in source domains to perform tasks in target domains. These methods include instance-based and feature-based DA, which aim to reduce domain discrepancies by reweighting samples or learning shared feature spaces. Recent advances in deep learning have led to the development of DDA methods that use deep networks to learn domain-invariant representations. The survey categorizes DDA into one-step and multi-step settings. One-step DA assumes the source and target domains are directly related, while multi-step DA uses intermediate domains to bridge the gap. DDA approaches are categorized based on training loss, including discrepancy-based, adversarial-based, and reconstruction-based methods. Discrepancy-based methods aim to reduce domain distribution differences, adversarial-based methods use discriminators to minimize domain differences, and reconstruction-based methods focus on data reconstruction to create shared representations. The paper also discusses various DDA techniques, including deep adaptation networks (DAN), joint adaptation networks (JAN), and residual transfer networks (RTN). These methods use different loss functions and architectures to improve domain adaptation performance. Adversarial-based approaches, such as domain adversarial neural networks (DANN) and adversarial discriminative domain adaptation (ADDA), use discriminators to minimize domain differences. Generative models, such as CoGAN, generate synthetic data to improve domain adaptation performance. The survey highlights the importance of domain-invariant representations in DDA and discusses various techniques for learning these representations. It also identifies challenges in current DDA methods, including the need for more flexible architectures and better handling of domain shifts. The paper concludes with a discussion of future research directions in DDA.Deep domain adaptation (DDA) is a learning technique that addresses the lack of labeled data by leveraging deep networks to learn transferable representations. Unlike traditional methods that use shallow features, DDA integrates domain adaptation into the deep learning pipeline. This survey provides a comprehensive overview of DDA methods in computer vision, highlighting four key contributions: (1) a taxonomy of DDA scenarios based on domain divergence properties, (2) categorization of DDA approaches by training loss, (3) overview of applications beyond image classification, and (4) identification of current method limitations and future directions. The paper discusses traditional domain adaptation (DA) methods, which rely on labeled data in source domains to perform tasks in target domains. These methods include instance-based and feature-based DA, which aim to reduce domain discrepancies by reweighting samples or learning shared feature spaces. Recent advances in deep learning have led to the development of DDA methods that use deep networks to learn domain-invariant representations. The survey categorizes DDA into one-step and multi-step settings. One-step DA assumes the source and target domains are directly related, while multi-step DA uses intermediate domains to bridge the gap. DDA approaches are categorized based on training loss, including discrepancy-based, adversarial-based, and reconstruction-based methods. Discrepancy-based methods aim to reduce domain distribution differences, adversarial-based methods use discriminators to minimize domain differences, and reconstruction-based methods focus on data reconstruction to create shared representations. The paper also discusses various DDA techniques, including deep adaptation networks (DAN), joint adaptation networks (JAN), and residual transfer networks (RTN). These methods use different loss functions and architectures to improve domain adaptation performance. Adversarial-based approaches, such as domain adversarial neural networks (DANN) and adversarial discriminative domain adaptation (ADDA), use discriminators to minimize domain differences. Generative models, such as CoGAN, generate synthetic data to improve domain adaptation performance. The survey highlights the importance of domain-invariant representations in DDA and discusses various techniques for learning these representations. It also identifies challenges in current DDA methods, including the need for more flexible architectures and better handling of domain shifts. The paper concludes with a discussion of future research directions in DDA.